Show simple item record

Lexical Selection for Cross-Language Applications: Combining LCS with WordNet

dc.contributor.authorDorr, Bonnie J.en_US
dc.contributor.authorKatsova, Mariaen_US
dc.description.abstractThis paper describes experiments for testing the power of large-scale resources for lexical selection in machine translation (MT) and cross-language information retrieval (CLIR). We adopt the view that verbs with similar argument structure share certain meaning components, but that those meaning components are more relevant to argument realization than to idiosyncratic verb meaning. We verify this by demonstrating that verbs with similar argument structure as encoded in Lexical Conceptual Structure (LCS) are rarely synonymous in WordNet. We then use the results of this work to guide our implementation of an algorithm for cross-language selection of lexical items, exploiting the strengths of each resource: LCS for semantic structure and WordNet for semantic content. We use the Parka Knowledge-Based System to encode LCS representations and WordNet synonym sets and we implement our lexical-selection algorithm as Parka-based queries into a knowledge base containing both information types. (Also cross-referenced as UMIACS-TR-98-49) (Also cross-referenced as LAMP-TR-021)en_US
dc.format.extent114154 bytes
dc.relation.ispartofseriesUM Computer Science Department; CS-TR-3933en_US
dc.relation.ispartofseriesUMIACS; UMIACS-TR-98-49en_US
dc.titleLexical Selection for Cross-Language Applications: Combining LCS with WordNeten_US
dc.typeTechnical Reporten_US
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_US
dc.relation.isAvailableAtUniversity of Maryland (College Park, Md.)en_US
dc.relation.isAvailableAtTech Reports in Computer Science and Engineeringen_US
dc.relation.isAvailableAtUMIACS Technical Reportsen_US

Files in this item


This item appears in the following Collection(s)

Show simple item record